Using Benchmarking and Regression Models for Predicting CNN Training Time on a GPU-Reference-Cited by-同舟云学术

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Using Benchmarking and Regression Models for Predicting CNN Training Time on a GPU

Published:2024-06-03 Issue: Volume: Page:8-15
ISSN:
Container-title:Proceedings of the 4th Workshop on Performance EngineeRing, Modelling, Analysis, and VisualizatiOn STrategy
language:
Short-container-title:

Author:

Bryzgalov Peter¹^ORCID,Maeda Toshiyuki¹^ORCID

Affiliation:

1. STAIR Lab, Chiba Institute of Technology, Narashino, Chiba, Japan

Funder

JSPS

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3660317.3660323

Reference13 articles.

1. Optuna

2. Performance prediction for convolutional neural networks on edge GPUs

3. Predicting How CNN Training Time Changes on Various Mini-Batch Sizes by Considering Convolution Algorithms and Non-GPU Time

4. Ermao Cai, Da-Cheng Juan, Dimitrios Stamoulis, and Diana Marculescu. 2017. NeuralPower: Predict and Deploy Energy-Efficient Convolutional Neural Networks. In Proceedings of the Ninth Asian Conference on Machine Learning. 622--637.

5. DNNMark

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